{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Permutation Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Team</th>\n",
       "      <th>Opponent</th>\n",
       "      <th>Goal Scored</th>\n",
       "      <th>Ball Possession %</th>\n",
       "      <th>Attempts</th>\n",
       "      <th>On-Target</th>\n",
       "      <th>Off-Target</th>\n",
       "      <th>Blocked</th>\n",
       "      <th>Corners</th>\n",
       "      <th>...</th>\n",
       "      <th>Yellow Card</th>\n",
       "      <th>Yellow &amp; Red</th>\n",
       "      <th>Red</th>\n",
       "      <th>Man of the Match</th>\n",
       "      <th>1st Goal</th>\n",
       "      <th>Round</th>\n",
       "      <th>PSO</th>\n",
       "      <th>Goals in PSO</th>\n",
       "      <th>Own goals</th>\n",
       "      <th>Own goal Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14-06-2018</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>5</td>\n",
       "      <td>40</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Group Stage</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14-06-2018</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>Russia</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Group Stage</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15-06-2018</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Group Stage</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15-06-2018</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>89.0</td>\n",
       "      <td>Group Stage</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15-06-2018</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>Iran</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Group Stage</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date          Team      Opponent  Goal Scored  Ball Possession %  \\\n",
       "0  14-06-2018        Russia  Saudi Arabia            5                 40   \n",
       "1  14-06-2018  Saudi Arabia        Russia            0                 60   \n",
       "2  15-06-2018         Egypt       Uruguay            0                 43   \n",
       "3  15-06-2018       Uruguay         Egypt            1                 57   \n",
       "4  15-06-2018       Morocco          Iran            0                 64   \n",
       "\n",
       "   Attempts  On-Target  Off-Target  Blocked  Corners  ...  Yellow Card  \\\n",
       "0        13          7           3        3        6  ...            0   \n",
       "1         6          0           3        3        2  ...            0   \n",
       "2         8          3           3        2        0  ...            2   \n",
       "3        14          4           6        4        5  ...            0   \n",
       "4        13          3           6        4        5  ...            1   \n",
       "\n",
       "   Yellow & Red  Red  Man of the Match  1st Goal        Round  PSO  \\\n",
       "0             0    0               Yes      12.0  Group Stage   No   \n",
       "1             0    0                No       NaN  Group Stage   No   \n",
       "2             0    0                No       NaN  Group Stage   No   \n",
       "3             0    0               Yes      89.0  Group Stage   No   \n",
       "4             0    0                No       NaN  Group Stage   No   \n",
       "\n",
       "   Goals in PSO  Own goals  Own goal Time  \n",
       "0             0        NaN            NaN  \n",
       "1             0        NaN            NaN  \n",
       "2             0        NaN            NaN  \n",
       "3             0        NaN            NaN  \n",
       "4             0        1.0           90.0  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('data/FIFA 2018 Statistics.csv')  # 足球赛事数据集\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "怎么知道哪些特征影响多大\n",
    "\n",
    "破坏特征：比如把Goal Scored上下的顺序打乱，其它不变，然后比较破坏前和后的区别\n",
    "\n",
    "如果前 ≈ 后，说明这个特征对结果没影响\n",
    "如果后 ＜ 前，说明这个特征对结果影响起正作用\n",
    "如果后 ＞ 前，说明这个特征对结果影响起负作用（一般不会出现）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1    False\n",
       "2    False\n",
       "3     True\n",
       "4    False\n",
       "Name: Man of the Match, dtype: bool"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = (data['Man of the Match'] == 'Yes')  # 转换标签\n",
    "y[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Goal Scored</th>\n",
       "      <th>Ball Possession %</th>\n",
       "      <th>Attempts</th>\n",
       "      <th>On-Target</th>\n",
       "      <th>Off-Target</th>\n",
       "      <th>Blocked</th>\n",
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       "      <th>Offsides</th>\n",
       "      <th>Free Kicks</th>\n",
       "      <th>Saves</th>\n",
       "      <th>Pass Accuracy %</th>\n",
       "      <th>Passes</th>\n",
       "      <th>Distance Covered (Kms)</th>\n",
       "      <th>Fouls Committed</th>\n",
       "      <th>Yellow Card</th>\n",
       "      <th>Yellow &amp; Red</th>\n",
       "      <th>Red</th>\n",
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       "      <td>57</td>\n",
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       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>86</td>\n",
       "      <td>589</td>\n",
       "      <td>111</td>\n",
       "      <td>6</td>\n",
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      ],
      "text/plain": [
       "   Goal Scored  Ball Possession %  Attempts  On-Target  Off-Target  Blocked  \\\n",
       "0            5                 40        13          7           3        3   \n",
       "1            0                 60         6          0           3        3   \n",
       "2            0                 43         8          3           3        2   \n",
       "3            1                 57        14          4           6        4   \n",
       "4            0                 64        13          3           6        4   \n",
       "\n",
       "   Corners  Offsides  Free Kicks  Saves  Pass Accuracy %  Passes  \\\n",
       "0        6         3          11      0               78     306   \n",
       "1        2         1          25      2               86     511   \n",
       "2        0         1           7      3               78     395   \n",
       "3        5         1          13      3               86     589   \n",
       "4        5         0          14      2               86     433   \n",
       "\n",
       "   Distance Covered (Kms)  Fouls Committed  Yellow Card  Yellow & Red  Red  \\\n",
       "0                     118               22            0             0    0   \n",
       "1                     105               10            0             0    0   \n",
       "2                     112               12            2             0    0   \n",
       "3                     111                6            0             0    0   \n",
       "4                     101               22            1             0    0   \n",
       "\n",
       "   Goals in PSO  \n",
       "0             0  \n",
       "1             0  \n",
       "2             0  \n",
       "3             0  \n",
       "4             0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_names = [i for i in data.columns if data[i].dtype in [np.int64]]\n",
    "X = data[feature_names]\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "train_X, val_X,train_y,val_y = train_test_split(X, y,random_state=1)\n",
    "my_model = RandomForestClassifier(random_state=0).fit(train_X,train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <style>\n",
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       "    \n",
       "        <table class=\"eli5-weights eli5-feature-importances\" style=\"border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto;\">\n",
       "    <thead>\n",
       "    <tr style=\"border: none;\">\n",
       "        <th style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">Weight</th>\n",
       "        <th style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">Feature</th>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 80.00%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0750\n",
       "                \n",
       "                    &plusmn; 0.1159\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Goal Scored\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 82.40%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0625\n",
       "                \n",
       "                    &plusmn; 0.0791\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Corners\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 86.29%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0437\n",
       "                \n",
       "                    &plusmn; 0.0500\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Distance Covered (Kms)\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 87.69%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0375\n",
       "                \n",
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       "                \n",
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       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                On-Target\n",
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       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0375\n",
       "                \n",
       "                    &plusmn; 0.0468\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Free Kicks\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 92.42%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0187\n",
       "                \n",
       "                    &plusmn; 0.0306\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Blocked\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 94.29%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0125\n",
       "                \n",
       "                    &plusmn; 0.0750\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Pass Accuracy %\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 94.29%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0125\n",
       "                \n",
       "                    &plusmn; 0.0500\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Yellow Card\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 96.49%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0063\n",
       "                \n",
       "                    &plusmn; 0.0468\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Saves\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 96.49%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0063\n",
       "                \n",
       "                    &plusmn; 0.0250\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Offsides\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 96.49%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0063\n",
       "                \n",
       "                    &plusmn; 0.1741\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Off-Target\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 100.00%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0000\n",
       "                \n",
       "                    &plusmn; 0.1046\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Passes\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 100.00%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0\n",
       "                \n",
       "                    &plusmn; 0.0000\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Red\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 100.00%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0\n",
       "                \n",
       "                    &plusmn; 0.0000\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Yellow &amp; Red\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 100.00%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0\n",
       "                \n",
       "                    &plusmn; 0.0000\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Goals in PSO\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 89.16%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0312\n",
       "                \n",
       "                    &plusmn; 0.0884\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Fouls Committed\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 87.69%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0375\n",
       "                \n",
       "                    &plusmn; 0.0919\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Attempts\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 84.94%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0500\n",
       "                \n",
       "                    &plusmn; 0.0500\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Ball Possession %\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "    \n",
       "    </tbody>\n",
       "</table>\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import eli5 # pip install eli5\n",
    "from eli5.sklearn import PermutationImportance\n",
    "\n",
    "perm = PermutationImportance(my_model,random_state=1).fit(val_X, val_y)\n",
    "\n",
    "eli5.show_weights(perm, feature_names=val_X.columns.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Partial Dependence Plots\n",
    "\n",
    "特征重要性展示了每个特征发挥的作用情况，partial dependence plots 可以展示一个特征怎么影响预测结果\n",
    "\n",
    "模型建立完成后进行使用，概述如下：\n",
    "<ul>\n",
    "    <li>首先选中一个样本数据，此时想观察Ball Possession列对结果的影响。\n",
    "    <li>保证其它特征列不变，改变当前观察列的值，例如悬着40%，50%，60%分别进行预测，得到各自的结果。\n",
    "    <li>对比结果就能知道当前列（Ball Possession）对结果的影响情况。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x950 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from pdpbox import pdp, get_dataset, info_plots  # pip install pdpbox\n",
    "pdp_goals = pdp.pdp_isolate(model=my_model,\n",
    "                            dataset=val_X, \n",
    "                            model_features=feature_names,\n",
    "                            feature='Goal Scored')  # 观察的特征\n",
    "\n",
    "pdp.pdp_plot(pdp_goals, 'Goal Scored')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "y轴表示预测结果的变化对比于基本模型，由于在观察时不可能只看一个样本数据，肯定要选择多个样本数据，蓝色区域表示的是置信度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x684 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_to_plot = 'Distance Covered (Kms)'\n",
    "\n",
    "rf_model = RandomForestClassifier(random_state=0).fit(train_X, train_y)\n",
    "\n",
    "pdp_dist = pdp.pdp_isolate(model=rf_model, dataset=val_X,\n",
    "                          model_features=feature_names,\n",
    "                          feature=feature_to_plot)\n",
    "\n",
    "pdp.pdp_plot(pdp_dist,feature_to_plot)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 双特征观察\n",
    "需要先改源码：将Anaconda3\\Lib\\site-packages\\pdpbox 下 pdp_plot_utils.py文件中contour_label_fontsize=fontsize改成fontsize=fontsize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x684 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features_to_plot = ['Goal Scored', 'Distance Covered (Kms)']\n",
    "inter1 = pdp.pdp_interact(model=rf_model, dataset=val_X,\n",
    "                         model_features=feature_names,\n",
    "                         features=features_to_plot)\n",
    "\n",
    "pdp.pdp_interact_plot(pdp_interact_out=inter1, feature_names=features_to_plot,\n",
    "                     plot_type='contour')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以观察到，在跑动距离为100的情况下，进球数量越多，是不是意味着赢得可能性越大"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SHAP Values\n",
    "可以直观的展示每一个特征对结果走势的影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.3, 0.7]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row_to_show = 5\n",
    "data_for_prediction = val_X.iloc[row_to_show]\n",
    "data_for_prediction_array = data_for_prediction.values.reshape(1, -1)\n",
    "\n",
    "my_model.predict_proba(data_for_prediction_array)  # 能赢的概率为70%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "导入shap工具包可能出现问题，在Anaconda3\\Lib\\site-packages\\numpy\\lib\\arraypad.py中添加下面两个函数到_as_pairs和_as_pairs之间（956行）保存，重新加载即可，utf-8格式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _normalize_shape(ndarray, shape, cast_to_int=True):\n",
    "    ndims = ndarray.ndim\n",
    "    \n",
    "    if shape is None:\n",
    "        return ((None, None),) * ndims\n",
    "    \n",
    "    shape_arr = np.asarray(shape)\n",
    "    \n",
    "    try:\n",
    "            shape_arr = np.broadcast_to(shape_arr, (ndims, 2))\n",
    "    except ValueError:\n",
    "        fmt = \"Unable to create correctly shaped tuple from %s\"\n",
    "        raise ValueError(fmt % (shape,))\n",
    "    \n",
    "    if cast_to_int is True:\n",
    "        shape_arr = np.round(shape_arr).astype(int)\n",
    "        \n",
    "    return tuple(tuple(axis) for axis in shape_arr.tolist())\n",
    "\n",
    "\n",
    "def _validate_lengths(narray, number_elements):\n",
    "    normshp = _normalize_shape(narray, number_elements)\n",
    "    for i in normshp:\n",
    "        chk = [1 if x is None else x for x in i]\n",
    "        chk = [1 if x >= 0 else -1 for x in chk]\n",
    "        if (chk[0] > 0) or (chk[1] < 0):\n",
    "            fmt = \"%s cannot contain negative values.\"\n",
    "            raise ValueError(fmt % (number_elements,))\n",
    "    return normshp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import shap  # pip install shap\n",
    "\n",
    "#实例化\n",
    "explainer = shap.TreeExplainer(my_model)\n",
    "\n",
    "#计算\n",
    "shap_values = explainer.shap_values(data_for_prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "返回的SHAP values中包括了negative和positive两种情况，通常选择一种（positive）即可"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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n.childContextTypes?u(\"107\",this.getName()||\"ReactCompositeComponent\"):void 0;for(var i in e)i in n.childContextTypes?void 0:u(\"108\",this.getName()||\"ReactCompositeComponent\",i);return c({},t,e)}return t},_checkContextTypes:function(t,e,n){},receiveComponent:function(t,e,n){var r=this._currentElement,i=this._context;this._pendingElement=null,this.updateComponent(e,r,t,i,n)},performUpdateIfNecessary:function(t){null!=this._pendingElement?v.receiveComponent(this,this._pendingElement,t,this._context):null!==this._pendingStateQueue||this._pendingForceUpdate?this.updateComponent(t,this._currentElement,this._currentElement,this._context,this._context):this._updateBatchNumber=null},updateComponent:function(t,e,n,r,i){var o=this._instance;null==o?u(\"136\",this.getName()||\"ReactCompositeComponent\"):void 0;var a,c=!1;this._context===i?a=o.context:(a=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,a);var f=this._processPendingState(l,a),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,a):this._compositeType===_.PureClass&&(p=!m(s,l)||!m(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,a,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=a)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=c({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var u=r[a];c(o,\"function\"==typeof u?u.call(n,o,t,e):u)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(y(r,i))v.receiveComponent(n,i,t,this._processChildContext(e));else{var a=v.getHostNode(n);v.unmountComponent(n,!1);var u=d.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==d.EMPTY);this._renderedComponent=c;var s=v.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){l.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t,e=this._instance;return t=e.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==_.StatelessFunctional){f.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{f.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||t===!1||s.isValidElement(t)?void 0:u(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n?u(\"110\"):void 0;var r=e.getPublicInstance(),i=n.refs===g?n.refs={}:n.refs;i[t]=r},detachRef:function(t){var e=this.getPublicInstance().refs;delete e[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===_.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=x},function(t,e,n){\"use strict\";var r=n(4),i=n(358),o=n(163),a=n(24),u=n(11),c=n(371),s=n(387),l=n(167),f=n(395);n(1);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&(G[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML?v(\"137\",t._tag,t._currentElement._owner?\" Check the render method of \"+t._currentElement._owner.getName()+\".\":\"\"):void 0),null!=e.dangerouslySetInnerHTML&&(null!=e.children?v(\"60\"):void 0,\"object\"==typeof e.dangerouslySetInnerHTML&&V in e.dangerouslySetInnerHTML?void 0:v(\"61\")),null!=e.style&&\"object\"!=typeof e.style?v(\"62\",r(t)):void 0)}function o(t,e,n,r){if(!(r instanceof I)){var i=t._hostContainerInfo,o=i._node&&i._node.nodeType===H,u=o?i._node:i._ownerDocument;F(e,u),r.getReactMountReady().enqueue(a,{inst:t,registrationName:e,listener:n})}}function a(){var t=this;C.putListener(t.inst,t.registrationName,t.listener)}function u(){var t=this;S.postMountWrapper(t)}function c(){var t=this;A.postMountWrapper(t)}function s(){var t=this;P.postMountWrapper(t)}function l(){var t=this;t._rootNodeID?void 0:v(\"63\");var e=U(t);switch(e?void 0:v(\"64\"),t._tag){case\"iframe\":case\"object\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"video\":case\"audio\":t._wrapperState.listeners=[];for(var n in q)q.hasOwnProperty(n)&&t._wrapperState.listeners.push(k.trapBubbledEvent(n,q[n],e));break;case\"source\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e),k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topReset\",\"reset\",e),k.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function f(){N.postUpdateWrapper(this)}function p(t){Z.call(X,t)||($.test(t)?void 0:v(\"65\",t),X[t]=!0)}function h(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function d(t){var e=t.type;p(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var v=n(2),g=n(3),m=n(332),y=n(334),_=n(20),b=n(82),x=n(21),w=n(156),C=n(22),M=n(83),k=n(51),E=n(157),T=n(4),S=n(351),P=n(352),N=n(158),A=n(355),O=(n(9),n(364)),I=n(369),D=(n(8),n(54)),R=(n(0),n(94),n(80),n(96),n(1),E),L=C.deleteListener,U=T.getNodeFromInstance,F=k.listenTo,j=M.registrationNameModules,B={string:!0,number:!0},W=\"style\",V=\"__html\",z={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},H=11,q={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},Y={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},K={listing:!0,pre:!0,textarea:!0},G=g({menuitem:!0},Y),$=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,X={},Z={}.hasOwnProperty,Q=1;d.displayName=\"ReactDOMComponent\",d.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=Q++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(l,this);break;case\"input\":S.mountWrapper(this,o,e),o=S.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"option\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o);break;case\"select\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"textarea\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o),t.getReactMountReady().enqueue(l,this)}i(this,o);var a,f;null!=e?(a=e._namespaceURI,f=e._tag):n._tag&&(a=n._namespaceURI,f=n._tag),(null==a||a===b.svg&&\"foreignobject\"===f)&&(a=b.html),a===b.html&&(\"svg\"===this._tag?a=b.svg:\"math\"===this._tag&&(a=b.mathml)),this._namespaceURI=a;var p;if(t.useCreateElement){var h,d=n._ownerDocument;if(a===b.html)if(\"script\"===this._tag){var v=d.createElement(\"div\"),g=this._currentElement.type;v.innerHTML=\"<\"+g+\"></\"+g+\">\",h=v.removeChild(v.firstChild)}else h=o.is?d.createElement(this._currentElement.type,o.is):d.createElement(this._currentElement.type);else h=d.createElementNS(a,this._currentElement.type);T.precacheNode(this,h),this._flags|=R.hasCachedChildNodes,this._hostParent||w.setAttributeForRoot(h),this._updateDOMProperties(null,o,t);var y=_(h);this._createInitialChildren(t,o,r,y),p=y}else{var x=this._createOpenTagMarkupAndPutListeners(t,o),C=this._createContentMarkup(t,o,r);p=!C&&Y[this._tag]?x+\"/>\":x+\">\"+C+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"select\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return p},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(j.hasOwnProperty(r))i&&o(this,r,i,t);else{r===W&&(i&&(i=this._previousStyleCopy=g({},e.style)),i=y.createMarkupForStyles(i,this));var a=null;null!=this._tag&&h(this._tag,e)?z.hasOwnProperty(r)||(a=w.createMarkupForCustomAttribute(r,i)):a=w.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+w.createMarkupForRoot()),n+=\" \"+w.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=D(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return K[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&_.queueHTML(r,i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&_.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)_.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=S.getHostProps(this,o),a=S.getHostProps(this,a);break;case\"option\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"select\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"textarea\":o=A.getHostProps(this,o),a=A.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":S.updateWrapper(this);break;case\"textarea\":A.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(f,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(r===W){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else j.hasOwnProperty(r)?t[r]&&L(this,r):h(this._tag,t)?z.hasOwnProperty(r)||w.deleteValueForAttribute(U(this),r):(x.properties[r]||x.isCustomAttribute(r))&&w.deleteValueForProperty(U(this),r);for(r in e){var c=e[r],s=r===W?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(r===W)if(c?c=this._previousStyleCopy=g({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(j.hasOwnProperty(r))c?o(this,r,c,n):s&&L(this,r);else if(h(this._tag,e))z.hasOwnProperty(r)||w.setValueForAttribute(U(this),r,c);else if(x.properties[r]||x.isCustomAttribute(r)){var l=U(this);null!=c?w.setValueForProperty(l,r,c):w.deleteValueForProperty(l,r)}}a&&y.setValueForStyles(U(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=B[typeof t.children]?t.children:null,o=B[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return U(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"html\":case\"head\":case\"body\":v(\"66\",this._tag)}this.unmountChildren(t),T.uncacheNode(this),C.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return U(this)}},g(d.prototype,d.Mixin,O.Mixin),t.exports=d},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(96),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" \";if(t.useCreateElement){var c=n._ownerDocument,s=c.createComment(u);return o.precacheNode(this,s),i(s)}return t.renderToStaticMarkup?\"\":\"<!--\"+u+\"-->\"},receiveComponent:function(){},getHostNode:function(){return o.getNodeFromInstance(this)},unmountComponent:function(){o.uncacheNode(this)}}),t.exports=a},function(t,e,n){\"use strict\";var r={useCreateElement:!0,useFiber:!1};t.exports=r},function(t,e,n){\"use strict\";var r=n(81),i=n(4),o={dangerouslyProcessChildrenUpdates:function(t,e){var n=i.getNodeFromInstance(t);r.processUpdates(n,e)}};t.exports=o},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&f.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=c.executeOnChange(e,t);l.asap(r,this);var i=e.name;if(\"radio\"===e.type&&null!=i){for(var a=s.getNodeFromInstance(this),u=a;u.parentNode;)u=u.parentNode;for(var f=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<f.length;p++){var h=f[p];if(h!==a&&h.form===a.form){var d=s.getInstanceFromNode(h);d?void 0:o(\"90\"),l.asap(r,d)}}}return n}var o=n(2),a=n(3),u=n(156),c=n(85),s=n(4),l=n(11),f=(n(0),n(1),{getHostProps:function(t,e){var n=c.getValue(e),r=c.getChecked(e),i=a({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange});return i},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&u.setValueForProperty(s.getNodeFromInstance(t),\"checked\",n||!1);var r=s.getNodeFromInstance(t),i=c.getValue(e);if(null!=i){var o=\"\"+i;o!==r.value&&(r.value=o)}else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=s.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=f},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(158),c=(n(1),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){var n=a.getNodeFromInstance(t);n.setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length,a=o+r;return{start:o,end:a}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(392),l=n(168),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(2),i=n(3),o=n(81),a=n(20),u=n(4),c=n(54),s=(n(0),n(96),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \",s=\" /react-text \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var l=n._ownerDocument,f=l.createComment(o),p=l.createComment(s),h=a(l.createDocumentFragment());return a.queueChild(h,a(f)),this._stringText&&a.queueChild(h,a(l.createTextNode(this._stringText))),a.queueChild(h,a(p)),u.precacheNode(this,f),this._closingComment=p,h}var d=c(this._stringText);return t.renderToStaticMarkup?d:\"<!--\"+o+\"-->\"+d+\"<!--\"+s+\"-->\"},receiveComponent:function(t,e){if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n?r(\"67\",this._domID):void 0,8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(2),a=n(3),u=n(85),c=n(4),s=n(11),l=(n(0),n(1),{getHostProps:function(t,e){null!=e.dangerouslySetInnerHTML?o(\"91\"):void 0;var n=a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange});return n},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a?o(\"92\"):void 0,Array.isArray(c)&&(c.length<=1?void 0:o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;\n",
       "n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t?void 0:c(\"33\"),\"_hostNode\"in e?void 0:c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in t?void 0:c(\"35\"),\"_hostNode\"in e?void 0:c(\"35\");for(;e;){if(e===t)return!0;e=e._hostParent}return!1}function o(t){return\"_hostNode\"in t?void 0:c(\"36\"),t._hostParent}function a(t,e,n){for(var r=[];t;)r.push(t),t=t._hostParent;var i;for(i=r.length;i-- >0;)e(r[i],\"captured\",n);for(i=0;i<r.length;i++)e(r[i],\"bubbled\",n)}function u(t,e,n,i,o){for(var a=t&&e?r(t,e):null,u=[];t&&t!==a;)u.push(t),t=t._hostParent;for(var c=[];e&&e!==a;)c.push(e),e=e._hostParent;var s;for(s=0;s<u.length;s++)n(u[s],\"bubbled\",i);for(s=c.length;s-- 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r(){C||(C=!0,y.EventEmitter.injectReactEventListener(m),y.EventPluginHub.injectEventPluginOrder(u),y.EventPluginUtils.injectComponentTree(p),y.EventPluginUtils.injectTreeTraversal(d),y.EventPluginHub.injectEventPluginsByName({SimpleEventPlugin:w,EnterLeaveEventPlugin:c,ChangeEventPlugin:a,SelectEventPlugin:x,BeforeInputEventPlugin:o}),y.HostComponent.injectGenericComponentClass(f),y.HostComponent.injectTextComponentClass(v),y.DOMProperty.injectDOMPropertyConfig(i),y.DOMProperty.injectDOMPropertyConfig(s),y.DOMProperty.injectDOMPropertyConfig(b),y.EmptyComponent.injectEmptyComponentFactory(function(t){return new h(t)}),y.Updates.injectReconcileTransaction(_),y.Updates.injectBatchingStrategy(g),y.Component.injectEnvironment(l))}var i=n(331),o=n(333),a=n(335),u=n(337),c=n(338),s=n(341),l=n(343),f=n(346),p=n(4),h=n(348),d=n(356),v=n(354),g=n(357),m=n(361),y=n(362),_=n(367),b=n(372),x=n(373),w=n(374),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof 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v={_enabled:!0,_handleTopLevel:null,WINDOW_HANDLE:s.canUseDOM?window:null,setHandleTopLevel:function(t){v._handleTopLevel=t},setEnabled:function(t){v._enabled=!!t},isEnabled:function(){return v._enabled},trapBubbledEvent:function(t,e,n){return n?c.listen(n,e,v.dispatchEvent.bind(null,t)):null},trapCapturedEvent:function(t,e,n){return n?c.capture(n,e,v.dispatchEvent.bind(null,t)):null},monitorScrollValue:function(t){var e=a.bind(null,t);c.listen(window,\"scroll\",e)},dispatchEvent:function(t,e){if(v._enabled){var n=i.getPooled(t,e);try{p.batchedUpdates(o,n)}finally{i.release(n)}}}};t.exports=v},function(t,e,n){\"use strict\";var r=n(21),i=n(22),o=n(50),a=n(86),u=n(159),c=n(51),s=n(161),l=n(11),f={Component:a.injection,DOMProperty:r.injection,EmptyComponent:u.injection,EventPluginHub:i.injection,EventPluginUtils:o.injection,EventEmitter:c.injection,HostComponent:s.injection,Updates:l.injection};t.exports=f},function(t,e,n){\"use strict\";var r=n(385),i=/\\/?>/,o=/^<\\!\\-\\-/,a={CHECKSUM_ATTR_NAME:\"data-react-checksum\",addChecksumToMarkup:function(t){var e=r(t);return o.test(t)?t:t.replace(i,\" \"+a.CHECKSUM_ATTR_NAME+'=\"'+e+'\"$&')},canReuseMarkup:function(t,e){var n=e.getAttribute(a.CHECKSUM_ATTR_NAME);n=n&&parseInt(n,10);var i=r(t);return i===n}};t.exports=a},function(t,e,n){\"use strict\";function r(t,e,n){return{type:\"INSERT_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:n,afterNode:e}}function i(t,e,n){return{type:\"MOVE_EXISTING\",content:null,fromIndex:t._mountIndex,fromNode:p.getHostNode(t),toIndex:n,afterNode:e}}function o(t,e){return{type:\"REMOVE_NODE\",content:null,fromIndex:t._mountIndex,fromNode:e,toIndex:null,afterNode:null}}function a(t){return{type:\"SET_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function u(t){return{type:\"TEXT_CONTENT\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function c(t,e){return e&&(t=t||[],t.push(e)),t}function s(t,e){f.processChildrenUpdates(t,e)}var l=n(2),f=n(86),p=(n(40),n(9),n(15),n(24)),h=n(342),d=(n(8),n(388)),v=(n(0),{Mixin:{_reconcilerInstantiateChildren:function(t,e,n){return h.instantiateChildren(t,e,n)},_reconcilerUpdateChildren:function(t,e,n,r,i,o){var a,u=0;return a=d(e,u),h.updateChildren(t,a,n,r,i,this,this._hostContainerInfo,o,u),a},mountChildren:function(t,e,n){var r=this._reconcilerInstantiateChildren(t,e,n);this._renderedChildren=r;var i=[],o=0;for(var a in r)if(r.hasOwnProperty(a)){var u=r[a],c=0,s=p.mountComponent(u,e,this,this._hostContainerInfo,n,c);u._mountIndex=o++,i.push(s)}return i},updateTextContent:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[u(t)];s(this,r)},updateMarkup:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[a(t)];s(this,r)},updateChildren:function(t,e,n){this._updateChildren(t,e,n)},_updateChildren:function(t,e,n){var r=this._renderedChildren,i={},o=[],a=this._reconcilerUpdateChildren(r,t,o,i,e,n);if(a||r){var u,l=null,f=0,h=0,d=0,v=null;for(u in a)if(a.hasOwnProperty(u)){var g=r&&r[u],m=a[u];g===m?(l=c(l,this.moveChild(g,v,f,h)),h=Math.max(g._mountIndex,h),g._mountIndex=f):(g&&(h=Math.max(g._mountIndex,h)),l=c(l,this._mountChildAtIndex(m,o[d],v,f,e,n)),d++),f++,v=p.getHostNode(m)}for(u in i)i.hasOwnProperty(u)&&(l=c(l,this._unmountChild(r[u],i[u])));l&&s(this,l),this._renderedChildren=a}},unmountChildren:function(t){var e=this._renderedChildren;h.unmountChildren(e,t),this._renderedChildren=null},moveChild:function(t,e,n,r){if(t._mountIndex<r)return i(t,e,n)},createChild:function(t,e,n){return r(n,e,t._mountIndex)},removeChild:function(t,e){return o(t,e)},_mountChildAtIndex:function(t,e,n,r,i,o){return t._mountIndex=r,this.createChild(t,n,e)},_unmountChild:function(t,e){var n=this.removeChild(t,e);return t._mountIndex=null,n}}});t.exports=v},function(t,e,n){\"use strict\";function r(t){return!(!t||\"function\"!=typeof t.attachRef||\"function\"!=typeof t.detachRef)}var i=n(2),o=(n(0),{addComponentAsRefTo:function(t,e,n){r(n)?void 0:i(\"119\"),n.attachRef(e,t)},removeComponentAsRefFrom:function(t,e,n){r(n)?void 0:i(\"120\");var o=n.getPublicInstance();o&&o.refs[e]===t.getPublicInstance()&&n.detachRef(e)}});t.exports=o},function(t,e,n){\"use strict\";var r=\"SECRET_DO_NOT_PASS_THIS_OR_YOU_WILL_BE_FIRED\";t.exports=r},function(t,e,n){\"use strict\";function r(t){this.reinitializeTransaction(),this.renderToStaticMarkup=!1,this.reactMountReady=o.getPooled(null),this.useCreateElement=t}var i=n(3),o=n(155),a=n(17),u=n(51),c=n(162),s=(n(9),n(53)),l=n(88),f={initialize:c.getSelectionInformation,close:c.restoreSelection},p={initialize:function(){var t=u.isEnabled();return u.setEnabled(!1),t},close:function(t){u.setEnabled(t)}},h={initialize:function(){this.reactMountReady.reset()},close:function(){this.reactMountReady.notifyAll()}},d=[f,p,h],v={getTransactionWrappers:function(){return d},getReactMountReady:function(){return this.reactMountReady},getUpdateQueue:function(){return l},checkpoint:function(){return this.reactMountReady.checkpoint()},rollback:function(t){this.reactMountReady.rollback(t)},destructor:function(){o.release(this.reactMountReady),this.reactMountReady=null}};i(r.prototype,s,v),a.addPoolingTo(r),t.exports=r},function(t,e,n){\"use strict\";function r(t,e,n){\"function\"==typeof t?t(e.getPublicInstance()):o.addComponentAsRefTo(e,t,n)}function i(t,e,n){\"function\"==typeof t?t(null):o.removeComponentAsRefFrom(e,t,n)}var o=n(365),a={};a.attachRefs=function(t,e){if(null!==e&&\"object\"==typeof e){var n=e.ref;null!=n&&r(n,t,e._owner)}},a.shouldUpdateRefs=function(t,e){var n=null,r=null;null!==t&&\"object\"==typeof 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       " <script>\n",
       "   if (window.SHAP) SHAP.ReactDom.render(\n",
       "    SHAP.React.createElement(SHAP.AdditiveForceVisualizer, {\"outNames\": [\"f(x)\"], \"baseValue\": 0.4979166666666667, \"outValue\": 0.6999999999999993, \"link\": \"identity\", \"featureNames\": [\"Goal Scored\", \"Ball Possession %\", \"Attempts\", \"On-Target\", \"Off-Target\", \"Blocked\", \"Corners\", \"Offsides\", \"Free Kicks\", \"Saves\", \"Pass Accuracy %\", \"Passes\", \"Distance Covered (Kms)\", \"Fouls Committed\", \"Yellow Card\", \"Yellow & Red\", \"Red\", \"Goals in PSO\"], \"features\": {\"0\": {\"effect\": 0.11623052712197358, \"value\": 2.0}, \"1\": {\"effect\": -0.05767236774972997, \"value\": 38.0}, \"2\": {\"effect\": -0.010500230341588914, \"value\": 13.0}, \"3\": {\"effect\": 0.05671805697267669, \"value\": 7.0}, \"4\": {\"effect\": 0.0005656327591159131, \"value\": 4.0}, \"5\": {\"effect\": -0.02781995298436135, \"value\": 2.0}, \"6\": {\"effect\": 0.05009299017646108, \"value\": 6.0}, \"7\": {\"effect\": -0.037227919647310745, \"value\": 1.0}, \"8\": {\"effect\": 0.03430811901021752, \"value\": 18.0}, \"9\": {\"effect\": 0.03016994870984204, \"value\": 1.0}, \"10\": {\"effect\": -0.00395718586722609, \"value\": 69.0}, \"11\": {\"effect\": 0.016500061823796583, \"value\": 399.0}, \"12\": {\"effect\": -0.019409036641124033, \"value\": 148.0}, \"13\": {\"effect\": 0.0369174894971916, \"value\": 25.0}, \"14\": {\"effect\": -0.0006207334415318634, \"value\": 1.0}, \"17\": {\"effect\": 0.01778793393493057, \"value\": 3.0}}, \"plot_cmap\": \"RdBu\", \"labelMargin\": 20}),\n",
       "    document.getElementById('iE4AQSJICCK5FJC7A8XIR')\n",
       "  );\n",
       "</script>"
      ],
      "text/plain": [
       "<shap.plots._force.AdditiveForceVisualizer at 0x264e73aad68>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shap.initjs()\n",
    "# shap_values[1]指定为positive，0为negative\n",
    "shap.force_plot(explainer.expected_value[1], shap_values[1], data_for_prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0.70表示赢得概率，红色表示能够朝1得方向发展得，蓝色表示能够朝0得方向发展的；\n",
    "\n",
    "即红色的值越大越容易赢，蓝色值越大越容易输；\n",
    "\n",
    "多个深红色小块之间，区域越大，表示影响结果的能力越大。蓝色同理。如图中绿框区域\n",
    "<img src=\"assets/20201212155754.png\" width=\"50%\">\n",
    "\n",
    "这里的结果是选了样本，不够全面"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x626.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "explainer = shap.TreeExplainer(my_model)\n",
    "\n",
    "shap_values = explainer.shap_values(val_X)  # 选择全部样本\n",
    "\n",
    "shap.summary_plot(shap_values[1], val_X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Y轴是特征，X轴是正负，正的表示特征对结果起促进作用，负的表示特征对结果起抑制作用。右边Y轴表示越高，对结果影响越大。\n",
    "\n",
    "如上图中：\n",
    "    Goal Scored 左边区域的蓝色：进球越少就越抑制赢球这一结果。\n",
    "    Goal Scored 右边区域的红色：进球越多就越促进赢球这一结果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
